---name: phoenix-evalsdescription: Build and run evaluators for AI/LLM applications using Phoenix.license: Apache-2.0compatibility: Requires Phoenix server. Python skills need phoenix and openai packages; TypeScript skills need @arizeai/phoenix-client.metadata: author: oss@arize.comversion: 1.0.0languages: Python, TypeScript---

Phoenix Evals

Build evaluators for AI/LLM applications. Code first, LLM for nuance, validate against humans.

Quick Reference

Task	Files
Setup	setup-python, setup-typescript
Decide what to evaluate	evaluators-overview
Choose a judge model	fundamentals-model-selection
Use pre-built evaluators	evaluators-pre-built
Build code evaluator	evaluators-code-python, evaluators-code-typescript
Build LLM evaluator	evaluators-llm-python, evaluators-llm-typescript, evaluators-custom-templates
Batch evaluate DataFrame	evaluate-dataframe-python
Run experiment	experiments-running-python, experiments-running-typescript
Run evals in a test runner (CI gate)	integrations-pytest, integrations-vitest-jest
Create dataset	experiments-datasets-python, experiments-datasets-typescript
Generate synthetic data	experiments-synthetic-python, experiments-synthetic-typescript
Validate evaluator accuracy	validation, validation-evaluators-python, validation-evaluators-typescript
Export spans	observe-tracing-setup
Write a span filter (SpanQuery().where)	filter-expressions
Sample traces for review	observe-sampling-python, observe-sampling-typescript
Analyze errors	error-analysis, error-analysis-multi-turn, axial-coding
RAG evals	evaluators-rag
Avoid common mistakes	common-mistakes-python, fundamentals-anti-patterns
Production	production-overview, production-guardrails, production-continuous

Workflows

Starting Fresh: observe-tracing-setup → error-analysis → axial-coding → evaluators-overview

Building Evaluator: fundamentals → common-mistakes-python → evaluators-{code|llm}-{python|typescript} → validation-evaluators-{python|typescript}

RAG Systems: evaluators-rag → evaluators-code-* (retrieval) → evaluators-llm-* (faithfulness)

Gating CI: evaluators-{code|llm}-{python|typescript} → integrations-{pytest|vitest-jest} → production-continuous

Production: production-overview → production-guardrails → production-continuous

Reference Categories

Prefix	Description
fundamentals-*	Types, scores, anti-patterns
observe-*	Tracing, sampling
error-analysis-*	Finding failures
axial-coding-*	Categorizing failures
evaluators-*	Code, LLM, RAG evaluators
experiments-*	Datasets, running experiments
integrations-*	Run evals from test runners (pytest, Vitest, Jest) as a CI gate
validation-*	Validating evaluator accuracy against human labels
production-*	CI/CD, monitoring

Key Principles

Principle	Action
Error analysis first	Can't automate what you haven't observed
Custom > generic	Build from your failures
Code first	Deterministic before LLM
Validate judges	>80% TPR/TNR
Binary > Likert	Pass/fail, not 1-5
Invariants gate, signals trend	assert/expect hard invariants (CI red); log LLM-judge quality signals and gate the aggregate (acceptance criteria), not every case